Relevant bibliographies by topics / XGBoost

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'XGBoost'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'XGBoost.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "XGBoost"

1

Zeng, Fanchao, Qing Gao, Lifeng Wu, et al. "Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model." Atmosphere 16, no. 4 (2025): 419. https://doi.org/10.3390/atmos16040419.

Full text
Abstract:
Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter t
APA, Harvard, Vancouver, ISO, and other styles
2

OUKHOUYA, HASSAN, HAMZA KADIRI, KHALID EL HIMDI, and RABY GUERBAZ. "Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models." Statistics, Optimization & Information Computing 12, no. 1 (2023): 200–209. http://dx.doi.org/10.19139/soic-2310-5070-1822.

Full text
Abstract:

 
 
 Forecasting time series is crucial for financial research and decision-making in business. The nonlinearity of stock market prices profoundly impacts global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan, French, German, British, US, and Hong Kong markets, respectively. We compare the performance of machine learning models, including Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and the hybrid LSTM-XGBoost, a
APA, Harvard, Vancouver, ISO, and other styles
3

Prastiyo, Isnan Wisnu, and Arafat Febriandirza. "Analisis Perbandingan Prediksi Tingkat Kemiskinan Menggunakan Metode XGBoost dan Random Forest Regression." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 3 (2024): 1694. http://dx.doi.org/10.30865/mib.v8i3.7892.

Full text
Abstract:
This research aims to compare the performance of two prediction algorithms, XGBoost Regression and Random Forest Regression, in predicting poverty levels in the DKI Jakarta area. For this research, researchers obtained data from the DKI Jakarta Central Statistics Agency (BPS) covering the period 2010 to 2023. The testing method used involved measuring Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) to assess the accuracy of predictions from the two algorithms. . The findings show that the Random Forest Regression algorithm generally produces more accurate predictions compare
APA, Harvard, Vancouver, ISO, and other styles
4

Tran, Thanh-Ngoc, and Quoc-Dai Nguyen. "Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18849–54. https://doi.org/10.48084/etasr.8863.

Full text
Abstract:
Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum lo
APA, Harvard, Vancouver, ISO, and other styles
5

Zhao, Tianwen, Guoqing Chen, Sujitta Suraphee, Tossapol Phoophiwfa, and Piyapatr Busababodhin. "A hybrid TCN-XGBoost model for agricultural product market price forecasting." PLOS One 20, no. 5 (2025): e0322496. https://doi.org/10.1371/journal.pone.0322496.

Full text
Abstract:
Price volatility in agricultural markets is influenced by seasonality, supply-demand fluctuations, policy changes, and climate. These factors significantly impact agricultural production and the broader macroeconomy. Traditional time series models, limited by linear assumptions, often fail to capture the nonlinear nature of price fluctuations. To address this limitation, we propose an integrated forecasting model that combines TCN and XGBoost to improve the accuracy of agricultural price volatility predictions. TCN captures both short-term and long-term dependencies using convolutional operati
APA, Harvard, Vancouver, ISO, and other styles
6

Yang, Hao, Jiaxi Li, Siru Liu, Xiaoling Yang, and Jialin Liu. "Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation." JMIR Medical Informatics 10, no. 6 (2022): e36958. http://dx.doi.org/10.2196/36958.

Full text
Abstract:
Background Hypoglycemia is a common adverse event in the treatment of diabetes. To efficiently cope with hypoglycemia, effective hypoglycemia prediction models need to be developed. Objective The aim of this study was to develop and validate machine learning models to predict the risk of hypoglycemia in adult patients with type 2 diabetes. Methods We used the electronic health records of all adult patients with type 2 diabetes admitted to West China Hospital between November 2019 and December 2021. The prediction model was developed based on XGBoost and natural language processing. F1 score, a
APA, Harvard, Vancouver, ISO, and other styles
7

Makarand, Bhosale Sakshi. "Machine Learning Enabled Inventory Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35109.

Full text
Abstract:
Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging histo
APA, Harvard, Vancouver, ISO, and other styles
8

Makarand, Bhosale Sakshi. "Machine Learning Enabled Inventory Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30009.

Full text
Abstract:
Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging histo
APA, Harvard, Vancouver, ISO, and other styles
9

Gu, Kai, Jianqi Wang, Hong Qian, and Xiaoyan Su. "Study on Intelligent Diagnosis of Rotor Fault Causes with the PSO-XGBoost Algorithm." Mathematical Problems in Engineering 2021 (April 26, 2021): 1–17. http://dx.doi.org/10.1155/2021/9963146.

Full text
Abstract:
On basis of fault categories detection, the diagnosis of rotor fault causes is proposed, which has great contributions to the field of intelligent operation and maintenance. To improve the diagnostic accuracy and practical efficiency, a hybrid model based on the particle swarm optimization-extreme gradient boosting algorithm, namely, PSO-XGBoost is designed. XGBoost is used as a classifier to diagnose rotor fault causes, having good performance due to the second-order Taylor expansion and the explicit regularization term. PSO is used to automatically optimize the process of adjusting the XGBoo
APA, Harvard, Vancouver, ISO, and other styles
10

Harriz, Muhammad Alfathan, Nurhaliza Vania Akbariani, Harlis Setiyowati, and Handri Santoso. "Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction." Jambura Journal of Informatics 5, no. 1 (2023): 1–6. http://dx.doi.org/10.37905/jji.v5i1.18814.

Full text
Abstract:
This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuabl
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "XGBoost"

1

Alhomsi, Moaz, and Hinda Ahmed. "Forecasting of ExchangeRate: Autoregressive modelsvs. XGBoost." Thesis, Jönköping University, IHH, Nationalekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-51370.

Full text
Abstract:
In international economics and trading, the exchange rate is important. Forecasting theexchange rate helps in minimizing risks and maximizing profits. The study attempts to test threemodels to forecast EUR/USD exchange rate. Based on previous work by Meese & Rogoff(1983), we replicated the authors work of the Random Walk model of AR(1) on different periodand currency to check if the model was able to forecast the exchange rate. Then we ran ARDLand XGBoost models to find which of the two models performed better than the Random Walkmodel based on different measures. The measures are Root Mea
APA, Harvard, Vancouver, ISO, and other styles
2

Blomqvist, Johanna. "Using XGBoost to classify theBeihang Keystroke Dynamics Database." Thesis, Uppsala universitet, Datalogi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-357420.

Full text
Abstract:
Keystroke Dynamics enable biometric security systems by collecting and analyzing computer keyboard usage data. There are different approaches to classifying keystroke data and a method that has been gaining a lot of attention in the machine learning industry lately is the decision tree framework of XGBoost. XGBoost has won several Kaggle competitions in the last couple of years, but its capacity in the keystroke dynamics field has not yet been widely explored. Therefore, this thesis has attempted to classify the existing Beihang Keystroke Dynamics Database using XGBoost. To do this, keystroke
APA, Harvard, Vancouver, ISO, and other styles
3

Salam, Patrous Ziad. "Evaluating XGBoost for User Classification by using Behavioral Features Extracted from Smartphone Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233522.

Full text
Abstract:
Smartphones have opened the possibility to interact with people anytime and anywhere. A significant amount of individuals rely on their smartphone for work-related and everyday tasks. As a consequence modern smartphones include sensitive, valuable and confidential information, such as e-mails, photos, notes, and messages. The primary concern is to prevent unauthorized access to data stored on the smartphone and applications. Traditional authentication methods are entry-point based and do not support continuous authorization. Therefore, as long as the session is active, there are no mechanisms
APA, Harvard, Vancouver, ISO, and other styles
4

Elena, Podasca. "Predicting the Movement Direction of OMXS30 Stock Index Using XGBoost and Sentiment Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21119.

Full text
Abstract:
Background. Stock market prediction is an active yet challenging research area. A lot of effort has been put in by both academia and practitioners to produce accurate stock market predictions models, in the attempt to maximize investment objectives. Tree-based ensemble machine learning methods such as XGBoost have proven successful in practice. At the same time, there is a growing trend to incorporate multiple data sources in prediction models, such as historical prices and text, in order to achieve superior forecasting performance. However, most applications and research have so far focused o
APA, Harvard, Vancouver, ISO, and other styles
5

Hesselroth, Johannes. "Väderdata, Inverse Distance Weighting och XGBoost för prediktering av väglagsfriktion : En utforskande studie." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-74530.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Henriksson, Erik, and Kristopher Werlinder. "Housing Price Prediction over Countrywide Data : A comparison of XGBoost and Random Forest regressor models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302535.

Full text
Abstract:
The aim of this research project is to investigate how an XGBoost regressor compares to a Random Forest regressor in terms of predictive performance of housing prices with the help of two data sets. The comparison considers training time, inference time and the three evaluation metrics R2, RMSE and MAPE. The data sets are described in detail together with background about the regressor models that are used. The method makes substantial data cleaning of the two data sets, it involves hyperparameter tuning to find optimal parameters and 5foldcrossvalidation in order to achieve good performance e
APA, Harvard, Vancouver, ISO, and other styles
7

Cerna, Ñahuis Selene Leya. "Comparative analysis of XGBoost, MLP and LSTM techniques for the problem of predicting fire brigade Iiterventions /." Ilha Solteira, 2019. http://hdl.handle.net/11449/190740.

Full text
Abstract:
Orientador: Anna Diva Plasencia Lotufo<br>Abstract: Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and, as a result, they face an ever-increasing number of interventions, most of the time on constant resource. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters on constant resource is therefore to predict their workload, i.e., their n
APA, Harvard, Vancouver, ISO, and other styles
8

Hardin, Maria, and Joakim Wellenstam. "Utveckling av komplexitetsbedömningsmodell med XGBoost för att anpassa text-till-talsyntes inom Robot-Assisted Language Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263693.

Full text
Abstract:
Den sociala roboten Furhat har visat sig vara relevant för Robot-Assisted Language Learning. I dagsläget har Furhat en konstant talhastighet, men i tidigare studier har det framkommit att den skulle gynnas av att ha en mer dynamisk text-till-talsyntes. I denna kandidatexamensuppsats har en modell skapats, i syfte att klassificera meningar på svenska efter komplexitet. Modellen användes för att avgöra vilka meningar som bör ha en lägre talhastighet, i syfte att öka deras begripligheten för elever som lär sig svenska som andraspråk. För att utvärdera modellen jämfördes komplexitetsbedömningen me
APA, Harvard, Vancouver, ISO, and other styles
9

Федоров, Д. П. "Comparison of classifiers based on the decision tree." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16430.

Full text
Abstract:
The main purpose of this work is to compare classifiers. Random Forest and XGBoost are two popular machine learning algorithms. In this paper, we looked at how they work, compared their features, and obtained accurate results from their robots.
APA, Harvard, Vancouver, ISO, and other styles
10

Калайчев, Г. В. "Machine learning in classification tasks." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16433.

Full text
Abstract:
The main goal of this work is to show the ways to use machine learning algorithms to solve classification tasks. One of the most efficient algorithms is Gradient Boosting (XGB Classier). This is a method which is usually used in competitions because of his speed and opportunity to work with big amount of data.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "XGBoost"

1

Xgboost. the Extreme Gradient Boosting for Mining Applications. GRIN Verlag GmbH, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Quinto, Butch. Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More. Apress, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Nokeri, Tshepo Chris. Data Science Solutions with Python: Fast and Scalable Models Using Keras, Pyspark MLlib, H2O, XGBoost, and Scikit-Learn. Apress L. P., 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hands-On Gradient Boosting with XGBoost and Scikit-learn: Perform Accessible Machine Learning and Extreme Gradient Boosting with Python. Packt Publishing, Limited, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "XGBoost"

1

de Souza, Fernanda Maria, Julia Grando, and Fabiano Baldo. "Adaptive Fast XGBoost for Regression." In Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21686-2_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gouveia, Arnaldo, and Miguel Correia. "Network Intrusion Detection with XGBoost." In Recent Advances in Security, Privacy, and Trust for Internet of Things (IoT) and Cyber-Physical Systems (CPS). Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429270567-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Montassar, Imen, Belkacem Chikhaoui, and Shengrui Wang. "Agitated Behaviors Detection in Children with ASD Using Wearable Data." In Digital Health Transformation, Smart Ageing, and Managing Disability. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_8.

Full text
Abstract:
AbstractChildren diagnosed with Autism Spectrum Disorder (ASD) often exhibit agitated behaviors that can isolate them from their peers. This study aims to examine if wearable data, collected during everyday activities, could effectively detect such behaviors. First, we used the Empatica E4 device to collect real data including Blood Volume Pulse (BVP), Electrodermal Activity (EDA), and Acceleration (ACC), from a 9-years-old male child with autism over 6 months. Second, we analyzed and extracted numerous features from each signal, and employed different classifiers including Support Vector Mach
APA, Harvard, Vancouver, ISO, and other styles
4

Abbasi, Raza Abid, Nadeem Javaid, Muhammad Nauman Javid Ghuman, Zahoor Ali Khan, Shujat Ur Rehman, and Amanullah. "Short Term Load Forecasting Using XGBoost." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15035-8_108.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Saadat, Sumaya, and V. Joseph Raymond. "Malware Classification Using CNN-XGBoost Model." In Artificial Intelligence Techniques for Advanced Computing Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5329-5_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Arora, Renuka, and Sunny Arora. "Speech impairment recognition using XGBoost classifier." In Artificial Intelligence and Speech Technology. CRC Press, 2021. http://dx.doi.org/10.1201/9781003150664-21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Korstanje, Joos. "Gradient Boosting with XGBoost and LightGBM." In Advanced Forecasting with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7150-6_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yang, Wenhan, Boyu Sun, and Banruo Liu. "Basic Profiling Extraction Based on XGBoost." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0713-5_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hussain, Nisar, Amna Qasim, Zia-ud-din Akhtar, et al. "Stock Market Performance Analytics Using XGBoost." In Advances in Computational Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47765-2_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Ghosh, Samyadeep, Dinesh Borse, Lakshmi Ram Kiran Padilam, et al. "Application of XGBoost in Flood Modeling." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9168-2_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "XGBoost"

1

Nageswari, Akula, U. Jyothi, G. Divya, T. Ammannamma, and V. Usha. "Water Quality Classification using XGBoost method." In 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). IEEE, 2024. https://doi.org/10.1109/icccmla63077.2024.10871422.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Quyet, Nguyen Huu, Tran Ngoc Hoa, Nguyen Ngoc Lan, and Bui Tien Thanh. "Damage Detection for Truss Bridge Structure Using XGBoost." In The 12th International Conference on Fracture Fatigue and Wear. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-jdc4vd.

Full text
Abstract:
Structural health monitoring (SHM) is a burgeoning area of interest among modern research endeavors, motivated by the application of state-of-the-art machine learning models. During the last few years, many researchers have proposed techniques for the analysis of SHM datasets, particularly those corresponding to sequence data collected from sensors. Following the flow of this research, in this work, we introduce an effective approach utilizing eXtreme Gradient Boosting (XGBoost), a potent ensemble learning framework rooted in gradient boosting for damage detection. A dataset of damage cases fr
APA, Harvard, Vancouver, ISO, and other styles
3

Andotra, Mansi, and Ramesh Kumar Sunkaria. "Early Detection of Arrhythmia Using XGBoost Model." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724550.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Husin, Husna Sarirah, Afizan Azman, Norhidayah Hamzah, and Pravin A.-L. Anand Kumar. "Early Prediction of Stroke using XGBoost classification." In 2024 International Visualization, Informatics and Technology Conference (IVIT). IEEE, 2024. http://dx.doi.org/10.1109/ivit62102.2024.10692877.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kumar, Suresh, Jyoti Prakash Singh, and Shantanu. "Genre-Based Movie Recommender System with XGBoost." In 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT). IEEE, 2024. https://doi.org/10.1109/c3it60531.2024.10829451.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhao, Wenchuan, Qi Zhang, and Ting Shu. "Air Pollution Source Tracing by VIME-XGBoost." In 2025 7th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2025. https://doi.org/10.1109/iscbi64586.2025.11015369.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lam, Ka Nam. "Traffic Prediction Using LSTM, RF and XGBoost." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013515600004619.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zhao, Hui, and Ling Zhou. "Emotion Recognition Based on XGBoost and HRV." In 2024 3rd International Conference on Health Big Data and Intelligent Healthcare (ICHIH). IEEE, 2024. https://doi.org/10.1109/ichih63459.2024.11064780.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Mukherjee, Dipendu, Shivam Chauhan, Sagarika Ghosh, and G. Uma Devi. "Enhancing Temporal Analysis of Jodhpur's Land Surface Temperature: A Comparative Study of XGBoost and Fine-Tuned XGBoost Model." In 2024 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks (IEMECON). IEEE, 2024. https://doi.org/10.1109/iemecon62401.2024.10846243.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Chen, Tianqi, and Carlos Guestrin. "XGBoost." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. http://dx.doi.org/10.1145/2939672.2939785.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "XGBoost"

1

Gu, Xiaofeng, A. Fedotov, and D. Kayran. Application of a machine learning algorithm (XGBoost) to offline RHIC luminosity optimization. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1777441.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Gu, Xiaofeng, T. Kanesue, M. Okamura, et al. EBIS BEAM INTENSITY ONLINE OPTIMIZATION WITH GPTUNE AND OFFLINE ANALYSIS WITH XGBOOST. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2331235.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

Full text
Abstract:
Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'XGBoost' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography